arXiv:cs.AI· Olukunle Owolabi, Pulkit Gupta, Fei Wang·· 4 小时前AI 评分34
何时记忆、何时弃权:面向可靠智能体记忆的类别条件化保留策略
When to Remember, When to Abstain: Category-Conditioned Retention for Reliable Agent Memory
AI 导读
研究提出按断言语义类别条件化设置置信度阈值,而非使用单一全局阈值,以决定智能体记忆是否保留该断言。在100个合成人物的冷启动记忆流水线上,4,715条候选断言中价值与信念类仅77.9%被来源支持,其他类别为96.2%。仅对价值类加严阈值后,无支持保留从6.2%降至4.0%(相对降低约36%),并在同等保留量下多保留约13个百分点覆盖率(95% CI 9.8–16.0)。
正文
Abstract:Persistent agent memory is only as reliable as its retention decision: an assertion weakly supported by its source can be stored and later reused as established fact. We study whether the retention decision should be governed by a confidence bar conditioned on the semantic category of the assertion rather than by a single global threshold, retaining well-evidenced categories liberally while abstaining more aggressively where inference is unreliable. We evaluate this in a deployed cold-start memory pipeline on 100 synthetic personas. The empirical evaluation is motivated by a sharp reliability asymmetry: across 4{,}715 candidate assertions, only 77.9\% of value and belief assertions are supported by their source, versus 96.2\% for all other categories. A global confidence threshold cannot separate these: it either admits unsupported value claims or discards well-evidenced ones. Conditioning the threshold on category resolves the tradeoff. In repeated held-out evaluation, a stricter bar on values alone reduces unsupported retentions from 6.2\% to 4.0\% (an ${\approx}36\%$ relative reduction, modest but consistent across folds) and, as corroborating evidence, preserves an estimated 13 percentage points more coverage (95\% CI 9.8--16.0) than a global threshold at comparable retention. Our results suggest that reliable retention depends on the type of assertion, not on confidence alone, and that a category-conditioned threshold can act as a simple, effective form of selective prediction at the write boundary.
| Comments: | 4 pages, 1 Figure, Accepted to NeurIPS 2026 Social Agent Workshop (this https URL) |
| Subjects: | Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2610.07100 [cs.AI] |
| (or arXiv:2610.07100v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07100 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Olukunle Owolabi [view email]
[v1]
Mon, 5 Oct 2026 14:36:51 UTC (313 KB)
来源:arXiv:cs.AI · arxiv.org